1. Field of the Invention
The present invention is directed towards a method of creating a realistic corpus of synthetic biologically based images subject to controlled constraints.
2. Background Art
The development of automated biological image processing applications including biometric identification and recognition, and classification for disease diagnoses and other medical research is restricted by the fact that there is a usually a lack of sufficient numbers of such images available for accurate study. Types of images can include, but are not restricted to, irises, magnetic resonance images (MRIs), x-rays, etc. The main reasons for the lack of such images are the high cost of image collection and, in the case of human subjects, the privacy and proprietary restrictions on sharing images. Furthermore, the limited set of images that exist and are actually available do not possess the systematically controlled variability necessary for thorough testing and evaluation of image processing algorithms. Therefore, it would be practically and financially beneficial to be able to synthesize a realistic corpus of such images.
Implicit in a image synthesis method is the process of quantitatively comparing one image with another. A problem in comparing two images is that traditional metrics of measuring mathematical distance between an original image and a synthesized or a distorted image do not adequately measure the perceptual correlation between the two images. That is, two images having a very small distance between them might actually look very different perceptually (when viewed by the human eye) and vice versa. This is because of the fact that human visual perception is highly adapted for extracting structural information from an image, which does not necessarily correlate with traditional mathematical distance metrics; hence the need exists for perceptually meaningful metrics for comparing images. Thus, what is also needed is a novel family of perceptually meaningful distance metrics for assessing full-reference image quality in the synthesis procedure. Besides the main purpose of synthesis, these metrics should also be applicable to content-based image retrieval (CBIR), described next.
With large image repositories, there is a need for an efficient means for searching and retrieving images based on particular image features of interest. Conventional approaches to image search and retrieval rely on annotations made to the images by a person a priori, but a CBIR system allows users to search a repository of images based on intrinsic image features (i.e., features that characterize edges, textures, and contours of interest) that are not necessarily captured by annotations or other supporting data. Traditional CBIR techniques for photographs that compare histograms of pixel colors/intensities are ineffective for the types of biological imagery described earlier because the histograms of individual images of these types tend to be too similar to provide discrimination. Accordingly, there is a further need for mathematically defined perceptually-based metrics that are more useful for comparing images for CBIR queries.